Papers with Code 아카이브

Semantic Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

29

평가표

150

보존된 평가 기록

1,918

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Hedgehogues/HoChiMinh

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/HengWeiBin/Oil-Polution-Dataset-with-PIDNet

PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers

https://github.com/HenonBamboo/PSPNet-MindSpore

Pyramid Scene Parsing Network

https://github.com/Hernandope/keras_resnet_FPN_cifar10

Feature Pyramid Networks for Object Detection

https://github.com/HiGal/Domain-Adaptation

Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

https://github.com/HiLab-git/MIDeepSeg

MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning

https://github.com/HibaKob/MicroBundlePillarTrack

Segment Anything

https://github.com/HighCWu/stylegan2-paddle

Efficient Attention: Attention with Linear Complexities

https://github.com/HimBegginer/test_liver

The Liver Tumor Segmentation Benchmark (LiTS)

https://github.com/Hiwyl/mmdetection-obj

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Hoang-it/orhcid_classification

YOLACT: Real-time Instance Segmentation

https://github.com/HobbitLong/PyContrast

What Makes for Good Views for Contrastive Learning?

https://github.com/Holmes1204/PyTorch_Unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Hong-Hyun-Seung/Mask_Classification

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/HongyangGao/PixelTCN

Pixel Deconvolutional Networks

https://github.com/Howard-coder191/Relation3D

SAS: Segment Any 3D Scene with Integrated 2D Priors

https://github.com/HqWei/Semantic-Segmentation-with-Full-Convolutional-Neural-Network

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Hsankesara/DeepResearch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Hshuqin/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/HuguesTHOMAS/KPConv-PyTorch

KPConv: Flexible and Deformable Convolution for Point Clouds

https://github.com/Hui-Li/multi-task-learning-example-PyTorch

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/Huiyu-Li/Three-stage-Curriculum-Learning

A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor Segmentation

https://github.com/HuynhThanhQuan/skin-detective

Microsoft COCO: Common Objects in Context

https://github.com/HyeonhoonLee/MAIC2021_Sleep

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

https://github.com/HyeonwooNoh/DeconvNet

Learning Deconvolution Network for Semantic Segmentation

https://github.com/HyeonwooNoh/caffe

Learning Deconvolution Network for Semantic Segmentation

https://github.com/HzFu/MNet_DeepCDR

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

https://github.com/HzcIrving/DeepLearning_PlayGround/tree/main/Swin-Transformer

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/HzcIrving/DeepLearning_PlayGround/tree/main/VIT

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

https://github.com/IAmSuyogJadhav/3d-mri-brain-tumor-segmentation-using-autoencoder-regularization

3D MRI brain tumor segmentation using autoencoder regularization

https://github.com/IAmSuyogJadhav/Brainy

Brain Tumor Segmentation with Deep Neural Networks

https://github.com/IBM/MAX-Object-Detector

Microsoft COCO: Common Objects in Context

https://github.com/IBM/NeuronAlignment

Deep Residual Learning for Image Recognition

https://github.com/IDEA-Research/Grounded-Segment-Anything

Segment Anything

https://github.com/IDEA-Research/detrex

End-to-End Object Detection with Transformers

https://github.com/IDEA-Research/detrex/blob/main/projects/h_deformable_detr/README.md

DETRs with Hybrid Matching

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.